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Volatility estimation for cryptocurrencies: Further evidence with jumps and structural breaks

Amélie Charles () and Olivier Darné ()
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Amélie Charles: Audencia Business School
Olivier Darné: LEMNA, University of Nantes

Authors registered in the RePEc Author Service: Olivier Darné

Economics Bulletin, 2019, vol. 39, issue 2, 954-968

Abstract: In this paper we study the daily volatility of four cryptocurrencies (BitCoin, Dash, LiteCoin, and Ripple) from June 2014 to November 2018. We first show that the cryptocurrency returns are strongly characterized by the presence of jumps as well as structural breaks (except Dash). Then, we estimate four GARCH-type models that capture short memory (GARCH), asymmetry (APARCH), strong persistence (IGARCH), and long memory (FIGARCH) from (i) original returns, (ii) jump-filtered returns, and (iii) jump-filtered returns with structural breaks. Results indicate the importance to take into account the jumps and structural breaks in modelling volatility of the cryptocurrencies. It appears that the cryptocurrency returns are well modelled by infinite persistence (BitCoin, Dash, and LiteCoin) or long memory (Ripple) with a Student-t distribution.

Keywords: Cryptocurrency; GARCH; volatility; jumps; breaks. (search for similar items in EconPapers)
JEL-codes: C2 G1 (search for similar items in EconPapers)
Date: 2019-04-26
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